retrieva-jp
/

bert-1.3b

@@ -1,199 +1,148 @@
 ---
 library_name: transformers
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 ---
 library_name: transformers
+license: apache-2.0
+language:
+- ja
+- en
 ---
+# Retrieva BERT Model
+The **RetrievaBERT** is the pre-trained Transformer Encoder using Megatron-LM.
+It is designed for use in Japanese.
 ## Model Details
 ### Model Description
+The **RetrievaBERT** is the pre-trained Transformer Encoder using Megatron-LM.
+It is designed for use in Japanese.
+This model offers several advanced features compared to traditional BERT models:
+- **PreNorm**: Improved stability during training.
+- **SwiGLU**: Enhanced activation function for better performance.
+- **Grouped-Query Attention (Multi-Query Attention)**: Efficient attention mechanism.
+- **Max Sequence Length**: 2048 tokens, allowing for longer context.
+- **Parameters**: 1.3 billion parameters.
+- **Pre-training Objective**: Only Masked Language Modeling (MLM), not Next Sentence Prediction (NSP).
+- **Token Type IDs**: Not used in this model.
+### Model Sources
+- **Developed by:** Retrieva, Inc.
+- **Model type:** Based on MegatronBERT Architecture.
+- **Language(s) (NLP):** Primarily Japanese (optional support for English).
+- **License:** Apache 2.0
 ## Uses
+This model can be used as a Masked Language Model (MLM).
+However, it is primarily intended to be fine-tuned on downstream tasks.
+Depending on your use case, follow the appropriate section below.
 ### Direct Use
+This model is pre-trained using Masked Language Modeling.
+The mask token used is `<MASK|LLM-jp>`.
+Note that you need to set `trust_remote_code` to `True` because RetrievaBERT uses a custom model implementation.
+Example code for direct use:
+```python
+from transformers import AutoModelForMaskedLM, AutoTokenizer, pipeline
+model_id = "retrieva-jp/bert-1.3b"
+model = AutoModelForMaskedLM.from_pretrained(model_id, trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+pipe = pipeline("fill-mask", model=model, tokenizer=tokenizer)
+text = "こんにちは！私の名前は<MASK|LLM-jp>です！"
+print(pipe(text))
+```
+### Downstream Use
+RetrievaBERT is compatible with Hugging Face's AutoModels.
+To fine-tune RetrievaBERT for your specific task, use the corresponding AutoModel class.
+For detailed configuration, refer to the config.json file.
 ## Training Details
 ### Training Data
+The Retrieva BERT model was pre-trained on the reunion of five datasets:
+- [Japanese CommonCrawl Dataset by LLM-jp](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v2).
+- [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb).
+- Chinese Wikipedia dumped on 20240120.
+- Korean Wikipedia dumped on 20240120.
+- [The Stack](https://huggingface.co/datasets/bigcode/the-stack)
+The model was trained on 180 billion tokens using the above dataset.
 ### Training Procedure
+The model was trained on 4 to 32 H100 GPUs with a batch size of 1,024.
+We adopted the curriculum learning which is similar to the Sequence Length Warmup and training with the following sequence lengths and number of steps.
+- The sequence length of 128: 31,000 steps.
+- The sequence length of 256: 219,000 steps.
+- The sequence length of 512: 192,000 steps.
+- The sequence length of 2048: 12,000 steps.
 #### Training Hyperparameters
+The model was trained on the following hyperparameters.
+- Learning rate: 1.5e-4.
+- Learning rate decay style: Linear.
+- Learning rate warmup fraction: 0.01
+- Minimum learning rate: 1e-6
+- Floating point expression: BF16
 ## Evaluation
+We fine-tuned the following models and evaluated them on the [JGLUE](https://github.com/yahoojapan/JGLUE) development set.
+We adjusted the learning rate and training epochs for each model and task in accordance with [the JGLUE paper](https://www.jstage.jst.go.jp/article/jnlp/30/1/30_63/_pdf/-char/ja).
+| Model                            | MARC-ja/acc | JSTS/pearson | JSTS/spearman | JNLI/acc | JSQuAD/EM | JSQuAD/F1 | JComQA/acc |
+| :--- |---:|---:|---:|---:|---:|---:|---:|
+| tohoku-nlp/bert-base-japanese-v3 | 0.957       | 0.914        | 0.876         | 0.906    | 0.878     | 0.946     | 0.849      |
+| tohoku-nlp/bert-large-japanese-v2| 0.959       | 0.916        | 0.877         | 0.901    | 0.884     | 0.951     | 0.867      |
+| ku-nlp/deberta-v3-base-japanese　　　　| 0.958       | 0.925        | 0.890         | 0.902    | 0.925     | 0.910     | 0.882      |
+| retrieva-jp/bert-1.3b　　　　　　　　　　　　　　　　　　　　　　　　| 0.952       | 0.916        | 0.877         | 0.896    | 0.916     | 0.879     | 0.815      |
+## Technical Specifications
+### Model Architectures
+The Retrieva BERT model is based on BERT with the following hyperparameters:
+- Number of layers: 48
+- Hidden layer size: 1536
+- FFN hidden layer size: 4096
+- Number of attention heads: 24
+- Maximum length of position embeddings: 2048
+As mentioned earlier, the main differences from the original BERT are:
+- PreNorm: Improved stability during training.
+- SwiGLU: Enhanced activation function for better performance.
+- Grouped-Query Attention (Multi-Query Attention): Efficient attention mechanism.
 ### Compute Infrastructure
+[TSUBAME 4](https://www.t4.gsic.titech.ac.jp/en/hardware)
+This model is based on results obtained from the TSUBAME deep-learning mini-camp.
 #### Software
+The model was trained using [Megatron-LM](https://github.com/NVIDIA/Megatron-LM).
+## More Information
+https://note.com/retrieva/n/n715bea2c2cd1 (in Japanese)
+## Model Card Authors
+Satoru Katsumata, Daisuke Kimura, Jiro Nishitoba
 ## Model Card Contact
+[email protected]